The presence and induction of regioselective dehydroxylases dictate urolithin metabolism by Enterocloster species
Bibliographic record
Abstract
Urolithins are a class of bioactive metabolites derived from the metabolism of dietary ellagitannins by the human gut microbiota. In the gut, urolithins are dehydroxylated regioselectively based on microbiota composition and activity. A single 9-hydroxy urolithin dehydroxylase (ucd) operon in gut resident Enterocloster species has been described to date; however, most enzymes in the urolithin metabolic pathway remain uncharacterized. Here, we investigate urolithin cross-feeding between members of the gut microbiota and discover a novel urolithin dehydroxylase in a subset of Enterocloster species. We show that urolithin intermediates, released by gut resident Gordonibacter species during ellagic acid metabolism, are dehydroxylated at both the 9- and 10-positions by E. asparagiformis, E. citroniae, and E. pacaense, but not E. bolteae. Using untargeted proteomics, we uncover a 10-hydroxy urolithin dehydroxylase operon, termed uxd, responsible for these species-specific differences in urolithin metabolism. By inducing uxd expression with diverse urolithins, we show that 9-hydroxy urolithins are required for uxd transcription and 10-position dehydroxylation. Collectively, this study reveals some of the genes, proteins, and substrate features underlying differences in urolithin metabolism by the human gut microbiota.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".